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  • Evaluation of Multiparameter Water Meter for Environmental Toolkit for Expeditionary Operations

    Purpose: A new, commercially available, field-portable water sensor was evaluated for efficacy during operation and compatibility with current Environmental Toolkit for Expeditionary Operations (ETEO) software. The ETEO provides sensors to Soldiers to rapidly identify and quantify environmental contamination in soil, air, and water at potential new base sites during initial reconnaissance to ensure Soldier safety and minimize unnecessary remediation efforts by the Army. The primary objective of this study was to enhance ETEO performance by providing the capability to evaluate multiple water quality properties simultaneously.
  • A Review of Empirical Algorithms for the Detection and Quantification of Harmful Algal Blooms Using Satellite-Borne Remote Sensing

    Abstract: Harmful Algal Blooms (HABs) continue to be a global concern, especially since predicting bloom events including the intensity, extent, and geographic location, remain difficult. However, remote sensing platforms are useful tools for monitoring HABs across space and time. The main objective of this review was to explore the scientific literature to develop a near-comprehensive list of spectrally derived empirical algorithms for satellite imagers commonly utilized for the detection and quantification HABs and water quality indicators. This review identified the 29 WorldView-2 MSI algorithms, 25 Sentinel-2 MSI algorithms, 32 Landsat-8 OLI algorithms, 9 MODIS algorithms, and 64 MERIS/Sentinel-3 OLCI algorithms. This review also revealed most empirical-based algorithms fell into one of the following general formulas: two-band difference algorithm (2BDA), three-band difference algorithm (3BDA), normalized-difference chlorophyll index (NDCI), or the cyanobacterial index (CI). New empirical algorithm development appears to be constrained, at least in part, due to the limited number of HAB-associated spectral features detectable in currently operational imagers. However, these algorithms provide a foundation for future algorithm development as new sensors, technologies, and platforms emerge.
  • Operations & Maintenance (O&M) Facility Data Exchange Pilot Expansion to BUILDER SMS

    Abstract: The Army has many enterprise Operation and Maintenance (O&M) systems that require manual input of the same facility data collected through-out the facility life cycle. This manual input of data costs Army installations valuable time and labor. A standardized approach to deliver the O&M information in a consistent, accurate, timely, and digital method for expedited input into the numerous systems is needed. A United States Army Corps of Engineers (USACE)-led team consisting of O&M subject matter experts within USACE and industry developed a standardized process for collecting and exchanging facility data for downstream applications. The process is defined in the Engineering and Construction Bulletin (ECB) 2018-6 and includes utilization of Unified Facilities Guide Specification (UFGS) 01 78 24.00 10. An initial pilot study verified that asset data collected during facility construction could effectively be imported into the Army General Fund Enterprise Business System (GFEBS). This second pilot study focused on facilitating the import of facility asset and equipment data collected during construction into the BUILDER Sustainment Management System (SMS) web-based software application. The project scope included investigation of current Army installations’ processes as they relate to BUILDER SMS as well as initial testing of information transfer approaches.
  • Inland Waterway Network Mapping of AIS Data for Freight Transportation Planning

    Abstract: Travel demand models (TDMs) with freight forecasts estimate performance metrics for competing infrastructure investments and potential policy changes. Unfortunately, freight TDMs fail to represent non-truck modes with levels of detail adequate for multi-modal infrastructure and policy evaluation. Recent expansions in the availability of maritime movement data, i.e. Automatic Identification System (AIS), make it possible to expand and improve representation of maritime modes within freight TDMs. AIS may be used to track vessel locations as timestamped latitude–longitude points. For estimation, calibration and validation of freight TDMs, this work identifies vessel trips by applying network mapping (map-matching) heuristics to AIS data. The automated methods are evaluated on a 747-mile inland waterway network, with AIS data representing 88% of vessel activity. Inspection of 3820 AIS trajectories was used to train the heuristic parameters including stop time, duration and location. Validation shows 84·0% accuracy in detecting stops at ports and 83·5% accuracy in identifying trips crossing locks. The resulting map-matched vessel trips may be applied to generate origin–destination matrices, calculate time impedances, etc. The proposed methods are transferable to waterways or maritime port systems, as AIS continues to grow.
  • Probabilistic Neural Networks that Predict Compressive Strength of High Strength Concrete in Mass Placements using Thermal History

    Abstract: This study explored the use of artificial neural networks to predict UHPC compressive strengths given thermal history and key mix components. The model developed herein employs Bayesian variational inference using Monte Carlo dropout to convey prediction uncertainty using 735 datapoints on seven UHPC mixtures collected using a variety of techniques. Datapoints contained a measured compressive strength along with three curing inputs (specimen maturity, maximum temperature experienced during curing, time of maximum temperature) and five mixture inputs to distinguish each UHPC mixture (ce-ment type, silicon dioxide content, mix type, water to cementitious material ratio, and admixture dosage rate). Input analysis concluded that predictions were more sensitive to curing inputs than mixture inputs. On average, 8.2% of experimental results in the final model fell outside of the predicted range with 67.9%of these cases conservatively underpredicting. The results support that this model methodology is able to make sufficient probabilistic predictions within the scope of the provided dataset but is not for extrapo-lating beyond the training data. In addition, the model was vetted using various datasets obtained from literature to assess its versatility. Overall this model is a promising advancement towards predicting mechanical properties of high strength concrete with known uncertainties.
  • Growth Assessments of Starry Stonewort (Nitellopsis obtusa) in Various Substrate Types for Large-scale Cultivation Studies

    Purpose: The purpose of this study was to compare multiple substrate types to optimize cultivation conditions for the invasive macroalga Nitellopsis obtusa (Desv. in Loisel.) J. Groves, commonly known as starry stonewort. Large-scale cultivation will allow for tiered approaches to management evaluation research while minimizing the influence of confounding variables.
  • Spatial and Temporal Variability of the Alligatorweed Pathogen, Alternaria alternantherae, in Louisiana

    Abstract: Alligatorweed leaf spot is a disease of invasive Alternanthera philoxeroides(Alligatorweed) in the southern US, caused by Alternaria alternantherae. However, little is known about when or where this pathogen naturally occurs. To better understand this species’life history, we examined temporal (every 2–3 weeks) and spatial (latitudinal) patterns of A. alternantherae occurrence at sites in Louisiana for 2 y. Pathogen presence reflectedclear within-year temporal and spatial patterns. Overall, the percentage of leaves infectedwith A. alternantherae was low during spring each year (0–20% infected) but increasedthroughout summer (maximum of 50% infected), and plants in northern sites had lowerfrequency of infection relative to southern sites until later in the year (late summer/early fall) but only in 1 of the 2 years of our study. The mean proportion of leaves infected with A. alternantherae declined with latitude both years (P = 0.01) and variability increasedwith latitude (P = 0.04), a pattern suggestive of range limitation in northern areas. We estimate a northern distributional limit of 34°N for A. alternantherae in Louisiana, but Alligatorweed occurs farther north. Although we did not directly examine disease impacts to Alligatorweed during the study, they may be greatest in southern areas, where the pathogenis more common early and throughout the growing season, and thus may be less likely to provide control in northern infestations of the invasive Alligatorweed.
  • Improving Container Shipment Analysis

    Abstract: US Army Corps of Engineers (USACE) deep-draft navigation economic analyses use assumptions about the sensitivity of vessel operations to channel modification to estimate national economic development benefits. The complexity and proprietary nature of carrier deployment decisions and loading practices adds uncertainty to USACE navigation studies. This report attempts to provide an overview of containership deployment and loading practices as it relates to USACE navigation studies to improve the quality of deep-draft economics. The report relies on trade data, vessel order books, and carrier interviews to study the impact of channel modification on vessel loading and deployment. The report makes recommendations for developing deployment and loading inputs for future economic evaluations.
  • Ballistic Protection Using Snow

    Abstract: Small (5.56 mm, 7.62 mm and 9 mm) and medium (12.7 mm) arms rounds were fired at snow-filled 1.5m cubic gabions in a mid-winter condition in Fairbanks, Alaska. The rounds were excavated and penetration by each ammunition type was measured. A distribution and average of penetration depth was determined. All 320 rounds fired were captured within 1.5m after entering the snow barrier. Comparison with published models of ballistics penetration of snow showed mixed results with several matching our data within 10% and all but one within 32%. However, most of these models are simplistic in that they accommodate limited variables and therefore may not be expected to perform well in all settings. We conclude that snow-based ballistics protection structures can be quickly and efficiently erected in suitable environments and with minimal size, can provide reliable protection against small and medium arms fire.
  • Southern Flying Squirrels (Glaucomys volans) as Major Predators of Avian Nest Boxes

    Abstract: Bird population dynamics are strongly affected by the ability to successfully reproduce, and nest predation is the primary cause of reproductive failure for most birds. Efforts to understand nest predation and manage its effects on species of conservation concern require knowledge of the ecology of associated predator assemblages. Recently, studies using cameras to record events at nests have illuminated this previously under-studied avian life stage, but such studies have been largely limited to open-cup nests. Cavity nests may be depredated by a different suite of predators and incubating or brooding females occupying such nests may be more vulnerable to predation relative to open-cup nests. Here, we used motion-activated, infrared trail cameras to record predators of artificial nest boxes in a Pinus palustris Mill. (Longleaf Pine) forest in southern Alabama. Although Glaucomys Volans L. (Southern Flying Squirrel) have only rarely been captured on film preying on nests, we found them to be responsible for the vast majority (84%) of bird-nest depredations at nest boxes, and these depredations contributed to a surprisingly low overall rate of nest success (~20%). These results may have implications for the conservation of birds that nest in artificial cavities in Longleaf Pine forests and highlight the importance of further studies on predator assemblages and their effects on nesting birds.